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United States Department of Agriculture

Agricultural Research Service

Title: Remote Sensing of Surface Energy Fluxes at 100 -M Pixel Resolutions

Authors
item Norman, John - UNIV OF WI-MADISON
item Anderson, Martha - UNIV OF WI-MADISON
item KUSTAS, WILLIAM
item French, Andrew - NASA GSFC
item Mecikalski, John - UNIV OF WI-MADISON
item Torn, Ray - UNIV OF WI-MADISON
item Diak, George - UNIV OF WI-MADISON
item Schmugge, Thomas
item Tanner, Bert - UNIV OF WI-MADISON

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 13, 2003
Publication Date: October 17, 2003
Citation: Norman, J.M., Anderson, M.C., Kustas, W.P, French, A.N., Mecikalski, J.R., Torn, R.D., Diak, G.R., Schmugge, T.J., Tanner, B.C.W. 2003. Remote sensing of surface energy fluxes at 10-m pixel resolutions. Water Resources Research. 39(8):1221-1261.

Interpretive Summary: Many applications exist within the fields of agriculture, forestry, land management, and hydrologic assessment for routine estimation of evapotranspiration (ET), at spatial resolutions on the order of 100 m. A new two-step approach (called the Disaggregated Atmosphere Land Exchange Inverse model or DisALEXI) has been developed to combine low- and high-resolution remote sensing data to estimate ET on the 10-100 -m scale without requiring any local observations. Using data from the Southern Great Plains field experiment of 1997, differences between remote estimates of ET and ground-based measurements were found to be comparable to accuracy of ET measurement techniques. The DisALEXI approach was useful for estimating field-scale ET in a heterogeneous area of central Oklahoma without using any local observations; thus providing a means for scaling kilometer-scale ET estimates down to plot size. Although the DisALEXI approach is promising for general applicability, further tests with varying surface conditions are necessary to establish greater confidence.

Technical Abstract: Many applications exist within the fields of agriculture, forestry, land management, and hydrologic assessment for routine estimation of surface energy fluxes, particularly evapotranspiration (ET), at spatial resolutions on the order of 100 m. A new two-step approach (called the Disaggregated Atmosphere Land Exchange Inverse model or DisALEXI) has been developed to combine low- and high-resolution remote sensing data to estimate ET on the 10-100 -m scale without requiring any local observations. The first step uses surface brightness-temperature-change measurements made over a 4-hour morning interval from the GOES satellite to estimate average surface fluxes on the scale of about 5 km with an algorithm known as ALEXI. The second step disaggregates the GOES 5-km surface-flux estimates by using high-spatial-resolution images of vegetation index and surface temperature, such as from ASTER, Landsat, MODIS or aircraft, to produce high-spatial-resolution maps of surface fluxes. Using data from the Southern Great Plains field experiment of 1997, the root-mean-square difference between remote estimates of surface fluxes and ground-based measurements is about 40 W m-2; comparable to uncertainties associated with surface flux measurement techniques. Although the DisALEXI approach is promising for general applicability, further tests with varying surface conditions are necessary to establish greater confidence.

Last Modified: 9/10/2014
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